Skip to main content

Who can spot an online romance scam?

Whitty, MT. (2019) — Journal of Financial Crime

DOI: 10.1108/jfc-06-2018-0053
Type:   Journal Article
Country:   United Kingdom
Tags:   offender tactics, psychology, AI misuse, risk factors, measurement, online romance scams, scam detection, dating profiles, romantic beliefs, impulsivity, response time, prevention training, UK sample, romance scam detection, online dating profiles, human detection, scam prevention

Synopsis (AI-Generated)

This study investigates factors that predict the ability to identify online romance scams, focusing on personality traits, belief systems, domain expertise, and response speed. In an online experiment, 261 participants were asked to judge whether a given profile represented a scam or was genuine. Beyond the labeling task, participants completed a set of assessments that captured their personality characteristics and belief orientations, and they answered demographic and descriptive questions. Response latency was recorded to capture how quickly participants made their judgments. The combined data were used to assess which individual differences and behavioral measures best distinguish fraudulent profiles from legitimate ones and to explore how these indicators relate to detection accuracy in a realistic online evaluation context. Results indicated that certain combinations of traits and experiences predicted greater accuracy in identifying scams. Specifically, lower scores on romantic belief scales, higher levels of impulsivity, and greater consideration of future consequences were associated with improved discrimination between fake and real profiles. Prior exposure to a romance scam also emerged as a positive predictor, as did longer response times during the evaluation task. Despite these associations, the overarching finding was that detecting romance scams remains a challenging task, with accuracy limited by the subtlety of scam cues and the variability of profiles.

Identified Gaps (AI-Generated)

Before this study, no research had tested whether psychological characteristics predict recognition of fake versus genuine online dating profiles. The stage model of romance scams had not empirically tested whether motivation to find an ideal partner explains susceptibility, and did not include the initial profile-authenticity decision. The authors also identify limited knowledge about novice versus expert deception detection in online settings.

Methods (AI-Generated)

An online Qualtrics study recruited 261 UK adults who had used dating and/or social-networking sites. Participants classified 20 randomly presented dating profiles (10 fake, 10 genuine), then completed demographic items, Romantic Beliefs, UPPS-R Impulsivity, and Consideration of Future Consequences scales. The study measured prior scam-spotting and cumulative response time. Forced-entry multiple regression tested whether these variables predicted profile-classification accuracy.

Limitations (AI-Generated)

The study used a UK online-panel sample and a constrained survey task involving static profiles rather than real-world interaction, communication, grooming, or financial requests. The regression explained 13% of accuracy variance, indicating substantial unexplained variation. These limitations are cautious inferences from the sampling frame, study procedure, and reported model fit.

Future Work (AI-Generated)

Test whether interactive scam-detection exercises improve accuracy more effectively than information-based e-safety guidance. Evaluate training that encourages users to take time reviewing profiles and examine whether this reduces romance-scam vulnerability. Further examine why impulsivity was associated with greater accuracy despite the opposite prediction.

Related Research Insights

See how this publication connects to RSRC's living evidence syntheses through current citations and research-topic mapping.

AI-Generated Content Notice

The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.

Found a possible error? Request a correction.